The concept of open innovation extends to every industry, everywhere. Life sciences and health care organizations are no exception, and we are commonly seeing the impact of openness with respect to the explosion of electronic health care data across the ecosystem.
Open innovation can drive the agenda to harness a wealth of extensive network data to help propel innovation in the life sciences industry. Life sciences companies that are new to real-world evidence (RWE) might want to start with our primer on the four shifts that are ushering in a new era of evidence in health care. In an increasingly resource-constrained industry such as health care, how do we openly innovate and move the needle forward? I would suggest (and evidence supports this claim) that the health care industry embrace open innovation. In this model, we could stop building data silos, and pool our resources for the ultimate common cause: improving patient lives.
I propose there are three tenets to adopting open innovation: open communities, open standards, and open software. Here’s how they work:
- Open communities are the people. They can be physicians, researchers, regulators, or anyone with a passion for health care. They can create the framework to drive projects. They can forge partnerships through which thought leadership can strategically guide innovation. The people who make up open communities might volunteer time or physical facilities that allow a forum where these conversations can be structured and shaped (e.g. Innovative Medicines Initiative (IMI), The Pistoia Alliance, OHDSI, Transcelerate, i2b2/transMART, CDISC).
- Open standards are the process. Health care data can be captured in a variety of ways. Electronic medical records, disease registries, clinical trials, and wearable monitors each have their own ways of translating personal health information into a machine. Open standards can offer leading practices for data integration and integration across these disparate sources. Open standards are often the cornerstone to data exchange, and can translate health records into related concepts (e.g., CDISC BRIDG, CDISC SDTM, SNOMED, OMOP Vocabularies).
- Open software is the technology. This is software for which the original source code is made freely available and may be redistributed and modified. Without licensing fees, open-source software can give equal ground, allowing researchers from all walks of funding access to rigorous technology to generate high quality, real-world evidence (e.g., i2b2, transMART, OHDSI, openMRS, REDCAP). Of course, there can be pros and cons to using open-source software. I would not suggest that it, by itself, is a panacea, as the coexistence of proprietary platforms combined with open source often leads to the right innovation mix.
One example of these tenets in action is Deloitte’s longstanding commitment to open collaborations serving as early developers in the i2b2/tranSMART Foundation (my colleague Brett Davis sits on the group’s board). My colleague David Hardison is chairman of CDISC’s board of directors. My colleagues and I occasionally give up our Saturdays to attend hackathons and collaboration meetings for the Observational Health Data Sciences and Informatics (OHDSI). These commitments and investments are indicative of our belief in the need for an open and sustainable evidence ecosystem, where vendors and consultants compete on the value and insights they can draw from data and not from controlling proprietary platforms or data.
Data can be a lifeblood. It can give us concrete points to observe our world, as long as we understand why we capture the data. (How many times do you write yourself a note and forget why you made that data point?) With the volume of data exploding, there is commonly a need to continuously advance our open communities, standards, and software to incorporate new technologies. Without a relevant common thread, insight can get lost in translation. Data can become meaningless.
In an open innovation framework, the community is often evolving. The Observational Medical Outcomes Partnership (OMOP) is an example. It began as a public-private partnership established to inform the appropriate use of observational health care databases for studying the effects of medical products. It was a deliberate effort to create standards that allow data to speak across different languages (e.g., disease definitions, procedures, drugs) and connect. It curated the knowledgebase that has become the cornerstone of many observational research initiatives, including the OMOP common data model (CDM) and the more than 85 OMOP standard vocabularies. Today using these standards more than 50 databases from over 20 countries are available in the OMOP CDM, accounting for approximately one-fifth of the world’s population and growing. You could pose your own research question and get back results connecting insights across more than 660 million patient lives.
Open innovation doesn’t necessarily stop there. It can be more than just connecting data. It’s likely time to shift the paradigm even further.
Many researchers regularly ask patients to consent to protocols or allow their data to be mined. Researchers often probe these patients at their near worst so we, as data and insight consumers, can one day understand their unique physiologies to advance a hypothesis or develop a molecule. It is on us, as data consumers, to help ensure that these individual investments of personal health information can be made as powerful as possible. It is us, the insight generators, that should owe it to patients to make it easier see their unique medical journey across the array of data sources that exist. It may take some nights and weekends. It may require some heart-to-hearts and suspension of self-interest, but it is likely time that open innovation made the proliferation of health information pay it forward.
Are you ready to join us in this journey?
To hear more about getting involved in ongoing efforts in open innovation, join Deloitte at the BioIT World 2017 in Boston, MA on Thursday, May 25 at 2:30PM where I will be leading a Track 4: Software Applications & Services panel on “Case Study: The Observational Health Data Sciences & Informatics Collaborative”.